Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F20%3A00504124" target="_blank" >RIV/67985556:_____/20:00504124 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21260/19:00333292
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-030-11292-9_34" target="_blank" >http://dx.doi.org/10.1007/978-3-030-11292-9_34</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-11292-9_34" target="_blank" >10.1007/978-3-030-11292-9_34</a>

Result language
angličtina
Original language name
Practical Initialization of Recursive Mixture-Based Clustering for Non-negative Data
Original language description
The paper provides a practical guide on initialization of the recursive mixture-based clustering of non-negative data. For modeling the non-negative data, mixtures of uniform, exponential, gamma and other distributions can be used. Initialization is known to be an important task for a start of the mixture estimation algorithm. Within the considered recursive approach, the key point of initialization is a choice of initial statistics of the involved prior distributions. The paper describes several initialization techniques for the mentioned types of components that can be beneficial primarily from a practical point of view.
Czech name
—
Czech description
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Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name
Informatics in Control, Automation and Robotics. ICINCO 2017.
ISBN
978-3-030-11292-9
Number of pages of the result
19
Pages from-to
679-698
Number of pages of the book
812
Publisher name
Springer
Place of publication
Cham
UT code for WoS chapter
000493283300034

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